This commentary explores the historical development and current status of dental ethics education in South Korean dental schools, which was explicitly initiated by the 2017 accreditation standards from the Korean Institute of Dental Education and Evaluation (KIDEE). Although now mandatory in all 11 dental schools in the country, dental ethics education remains in its early stages, with a focus on professional integrity, ethical decision-making, altruism, and rationality. To address the unique characteristics of dental practice, including preventive and aesthetic care, intensive chairside communication, and business-related ethical issues, specialized ethics modules should be incorporated into the curriculum. Continuous improvements in curriculum design, faculty training, and evaluation methods are urgently needed. Most importantly, ongoing research and active discourse on dental ethics within the dental community are essential to enhancing dental ethics education.

Informed consent for whole-genome sequencing (WGS)-based newborn screening presents distinctive ethical and legal challenges, particularly in balancing parental consent given on behalf of the child with the progressive realization of children’s rights. This study examines the issues discussed in the literature and proposes consent frameworks that are responsive to evolving clinical and societal contexts. A systematic literature review was conducted in accordance with PRISMA 2020 guidelines, analyzing 19 empirical, normative, and legal studies. The synthesis identified persistent tensions between safeguarding parental autonomy and ensuring the child’s future selfdetermination, alongside challenges in determining the scope, timing, and format of genomic information disclosure. International models increasingly adopt dynamic consent processes, such as staged consent or re-consent upon reaching legal capacity. These approaches illustrate the feasibility of aligning clinical utility, public health objectives, and rights-based ethics within largescale genomic programs. Based on its findings, this study recommends the implementation of staged consent mechanisms, transparent disclosure protocols, and procedural safeguards tailored to domestic legal and political frameworks. Such evolving consent practices are expected to support the development of ethically robust and socially sustainable governance for WGS-based newborn screening.

Objective: This study aims to systematically review the ethical and legal discussions regarding the utilization of artificial intelligence (AI) for patient triage and resource allocation in emergency medicine, and to identify the current state of discussions, their limitations, and future research directions. Methods: A comprehensive literature search was conducted following scoping review methodology. Relevant literature published after January 2020 was searched in the Web of Science, Scopus, CINAHL, PubMed, and Cochrane Library databases. Based on a PCC (population, concept, and context) framework (emergency patients/medical staff; triage, resource allocation; and emergency medicine with AI application), a final selection of 27 articles was analyzed. Results: The selected literature raised various ethical and legal issues related to the introduction of AI triage systems and AI utilization in emergency medicine, including data privacy, algorithmic bias, automation dependency, accountability, and explainability. In response to these issues, human-centered design, implementation of explainable AI, establishment of regulatory frameworks, continuous verification and evaluation, and ensuring human-in-the-loop were discussed as major solutions. However, discussions on the risks of “persuasive AI” that could mislead users, ethical issues of generative AI, and social validation and patient and public involvement were found to be insufficient. Conclusion Ethical and legal discussions regarding AI in emergency medicine are evolving toward seeking concrete solutions at technical, institutional, and relational dimensions. However, in-depth research on ethical challenges, such as reflecting the specificity of rapidly developing AI and the values of emergency medicine, is urgently required.

: This research focuses on the experiences of social workers who assist socially isolated households to prevent solitary deaths. The study aims to understand their support-related experiences, examining both the perceptions and practices of these workers. It highlights the importance of tailored support for isolated households, especially considering the unique challenges faced by middle-aged individuals in this demographic. Methods : The study employed purposive sampling to recruit social workers in Seoul who are actively engaged in supporting socially isolated households. Semi-structured interviews were conducted to gather in-depth insights into their experiences. The research methodology was rooted in qualitative analysis, specifically using Giorgi’s method. Results : Analysis of the interview data led to the identification of 12 sub-components and 5 upper components: “implementation of support for socially isolated households different from previous experience,” “feeling helpless in the face of inevitability,” “sympathy and communication with the heart,” “discovering challenges and opportunities in the field,” and “slowly, waiting for change.” These findings underscored the complexities and emotional challenges faced by social workers. Conclusion : The study highlights a significant gap in resources and manpower for supporting isolated households. It suggests the need for long-term, specially designed support systems, emphasizing improvements to better aid socially isolated individuals and the social workers who support them.

There is a more urgent call for educational methods of machine learning in medical education, and therefore, new approaches of teaching and researching machine learning in medicine are needed. This paper presents a case using machine learning through text analysis. Topic modeling of news articles with the keyword ‘asbestos’ were examined. Two hypotheses were tested using this method, and the process of machine learning of texts is illustrated through this example. Using an automated text analysis method, all the news articles published from January 1, 1990 to November 15, 2016 in South Korea which included ‘asbestos’ in the title and the body were collected by web scraping. Differences in topics were analyzed by structured topic modelling (STM) and compared by press companies and periods. More articles were found in liberal media outlets. Differences were found in the number and types of topics in the articles according to the partisanship and period. STM showed that the conservative press views asbestos as a personal problem, while the progressive press views asbestos as a social problem. A divergence in the perspective for emphasizing the issues of asbestos between the conservative press and progressive press was also found. Social perspective influences the main topics of news stories. Thus, the patients' uneasiness and pain are not presented by both sources of media. In addition, topics differ between news media sources based on partisanship, and therefore cause divergence in readers' framing. The method of text analysis and its strengths and weaknesses are explained, and an application for the teaching and researching of machine learning in medical education using the methodology of text analysis is considered. An educational method of machine learning in medical education is urgent for future generations.
Asbestos ; Education, Medical ; Humans ; Korea ; Machine Learning ; Methods ; Social Problems ; Social Responsibility